Big data-based power load analysis method and system

Abstract

The big data-based power load analysis method and system provided in this application obtains power detection information of target power flow data, performs current load analysis on the power detection information, determines the current flow events expressed by the power detection information, and then reads whether there is a current overload problem in the target power flow data based on the current flow events expressed by the power detection information. It reads the potential current overload problem in the target power flow data and avoids equipment malfunctions caused by the need to analyze specific events of the target power flow data for current overload problems. Therefore, it is necessary to perform real-time analysis of whether the current is overloaded to ensure the normal operation of the equipment.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and more specifically, to a method and system for power load analysis based on big data. Background Technology

[0002] With the continuous development and progress of big data analytics technology, its application in various fields is becoming increasingly widespread. However, when big data analytics technology is applied to power loads, it may encounter certain interferences, making it difficult to accurately determine current overload conditions. Therefore, a technical solution is urgently needed to address these technical problems. Summary of the Invention

[0003] To address the technical problems existing in related technologies, this application provides a power load analysis method and system based on big data.

[0004] Firstly, a method for power load analysis based on big data is provided. The method includes at least: obtaining target power flow data, where the target power flow data is an event awaiting power reading; obtaining power detection information of the target power flow data, where the power detection information is information generated by a detection platform when performing current detection on the target power flow data; performing current load analysis on the power detection information to determine the current load analysis result, where the current load analysis result indicates the correlation between the power detection information and the pending power reading result, and the pending power reading result indicates the power load relationship existing in the target power flow data; and combining the current load analysis result to obtain the power load analysis result of the target power flow data.

[0005] In one standalone embodiment, the step of performing current load analysis on the power detection information and determining the current load analysis result includes: inputting the power detection information into a current load analysis thread; performing current load analysis on the power detection information based on the current load analysis thread, and outputting the obtained current load analysis result, wherein the current load analysis thread is a current analysis thread pre-configured and determined through a historical database.

[0006] In one standalone embodiment, the step of performing current load analysis on the power detection information based on the current load analysis thread and outputting the current load analysis result includes: performing convolution processing and information optimization processing on the power detection information based on the current load analysis thread to determine the power detection information description; inputting the power detection information description into a trigger thread, and outputting the correlation between the pending power reading result and the power detection information to determine the current load analysis result.

[0007] In one standalone embodiment, the method further includes: obtaining example power detection information, the example power detection information including events for detecting power flow data described by objects in a power flow data architecture; updating the example power detection information to determine example data; and configuring the current load analysis thread in conjunction with the example data until the analysis result of the current load analysis thread meets a specified analysis instruction.

[0008] In one standalone embodiment, the step of updating the example power detection information to determine example data includes: filtering the example power detection information based on a specified filtering method to determine filtered example data; optimizing the example power detection information based on a specified optimization method to determine optimized example data; and obtaining the example data by combining the filtered example data and the optimized example data.

[0009] In one independently implemented embodiment, the specified filtering method includes one or more of the following: selecting example power detection information where the number of first default nodes is less than a specified number or a specified confidence level; selecting example power detection information where the first node type and the second node type are updated; selecting example power detection information where the number of similar events is greater than a specified number; selecting example power detection information where the number of second default nodes exceeds the constraint number or constraint confidence level; selecting example power detection information where each first node in the event is the same; and selecting example power detection information where the content of the event cannot be read.

[0010] In one independently implemented embodiment, the specified optimization method includes one or more of the following: debugging the example power detection information based on a specified debugging method; decompressing the example power detection information belonging to the first topic into the second topic, and then re-decompressing it into the first topic; correcting the text in the example power detection information based on a specified correction method.

[0011] In one standalone embodiment, obtaining the power load analysis result of the target power flow data by combining the current load analysis result includes: in response to the current load analysis result indicating that there is an association condition of pending power reading results satisfying the default association condition, performing a saliency description on the target power flow data; and loading the target power flow data into an optimization matrix for optimization based on the saliency description to obtain the power load analysis result of the target power flow data.

[0012] In one standalone embodiment, the method further includes: in response to the current load analysis results indicating that the correlation of the pending power readings is lower than the default correlation, acquiring an event that the target power flow data satisfies the analysis indication.

[0013] Secondly, a power load analysis system based on big data is provided, including a processor and a memory that communicate with each other. The processor is used to read a computer program from the memory and execute it to implement the above-mentioned method.

[0014] The power load analysis method and system based on big data provided in this application obtains the power detection information of the target power flow data, performs current load analysis on the power detection information, determines the current flow events expressed by the power detection information, and then reads whether there is a current overload problem in the target power flow data based on the current flow events expressed by the power detection information. It reads the potential current overload problems in the target power flow data and avoids equipment malfunctions caused by the need to analyze specific events in the target power flow data for current overload problems. Therefore, it is necessary to perform real-time analysis of whether the current is overloaded to ensure the normal operation of the equipment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a big data-based power load analysis method provided in an embodiment of this application.

[0017] Figure 2 This is a block diagram of a power load analysis device based on big data, provided as an embodiment of this application.

[0018] Figure 3 This is an architecture diagram of a big data-based power load analysis system provided in an embodiment of this application. Detailed Implementation

[0019] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0020] Please see Figure 1 This paper presents a power load analysis method based on big data, which may include the technical solutions described in steps 100-400.

[0021] Step 100: Obtain target power flow data, which is an event waiting to be read for power consumption.

[0022] Step 200: Obtain the power detection information of the target power flow data, wherein the power detection information is generated by the detection platform when it performs current detection on the target power flow data;

[0023] Step 300: Perform current load analysis on the power detection information and determine the current load analysis result. The current load analysis result is used to represent the correlation between the power detection information and the pending power reading result. The pending power reading result is used to represent the power load relationship existing in the target power flow data.

[0024] Step 400: Combine the current load analysis results to obtain the power load analysis results of the target power flow data.

[0025] It is understood that when performing the technical content described in steps 100-400 above, by obtaining the power detection information of the target power flow data and performing current load analysis on the power detection information, the current flow events expressed by the power detection information are determined. Based on the current flow events expressed by the power detection information, it is possible to read whether there is a current overload problem in the target power flow data. The potential current overload problem in the target power flow data is read to avoid equipment malfunctions caused by the need to analyze specific events in the target power flow data due to current overload problems. Therefore, it is necessary to perform real-time analysis of whether the current is overloaded to ensure the normal operation of the equipment.

[0026] In one possible implementation, the step of performing current load analysis on the power detection information and determining the current load analysis result includes: inputting the power detection information into a current load analysis thread; performing current load analysis on the power detection information based on the current load analysis thread, and outputting the obtained current load analysis result, wherein the current load analysis thread is a current analysis thread pre-configured and determined through a historical database.

[0027] In one possible implementation, the step of performing current load analysis on the power detection information based on the current load analysis thread and outputting the current load analysis result includes: performing convolution processing and information optimization processing on the power detection information based on the current load analysis thread to determine the power detection information description; inputting the power detection information description into a trigger thread, and outputting the correlation between the pending power reading result and the power detection information to determine the current load analysis result.

[0028] In one possible implementation, the method further includes: obtaining example power detection information, the example power detection information including events for detecting power flow data described by objects in the power flow data architecture; updating the example power detection information to determine example data; and configuring the current load analysis thread in conjunction with the example data until the analysis result of the current load analysis thread meets a specified analysis instruction.

[0029] In one possible implementation, the step of updating the example power detection information and determining example data includes: filtering the example power detection information based on a specified filtering method to determine filtered example data; optimizing the example power detection information based on a specified optimization method to determine optimized example data; and obtaining the example data by combining the filtered example data and the optimized example data.

[0030] In one possible implementation, the specified filtering method includes one or more of the following: selecting example power detection information where the number of first default nodes is less than a specified number or a specified confidence level; selecting example power detection information where the first node type and the second node type are updated; selecting example power detection information where the number of similar events is greater than a specified number; selecting example power detection information where the number of second default nodes exceeds the constraint number or constraint confidence level; selecting example power detection information where each first node in the event is the same; and selecting example power detection information where the content of the event cannot be read.

[0031] In one possible implementation, the specified optimization method includes one or more of the following: debugging the example power detection information based on a specified debugging method; decompressing the example power detection information belonging to the first topic into the second topic, and then re-decompressing it into the first topic; correcting the text in the example power detection information based on a specified correction method.

[0032] In one possible implementation, obtaining the power load analysis result of the target power flow data by combining the current load analysis result includes: in response to the current load analysis result indicating that there is an association situation where the association situation of the pending power reading result meets the default association situation, performing a saliency description on the target power flow data; and loading the target power flow data into an optimization matrix for optimization based on the saliency description to obtain the power load analysis result of the target power flow data.

[0033] In one possible implementation, the method further includes: in response to the current load analysis result indicating that the correlation of the pending power reading results is lower than the default correlation, acquiring an event that the target power flow data satisfies the analysis indication.

[0034] Based on the above, please refer to the following: Figure 2 A power load analysis device 200 based on big data is provided, which is applied to a power load analysis system based on big data. The device includes:

[0035] The data acquisition module 210 is used to acquire target power flow data, wherein the target power flow data is an event waiting to be read for power consumption;

[0036] Information generation module 220 is used to obtain power detection information of the target power flow data, wherein the power detection information is generated by the detection platform when it performs current detection on the target power flow data;

[0037] The relationship determination module 230 is used to perform current load analysis on the power detection information and determine the current load analysis result. The current load analysis result is used to represent the correlation between the power detection information and the pending power reading result. The pending power reading result is used to represent the power load relationship existing in the target power flow data.

[0038] The result analysis module 240 is used to combine the current load analysis results to obtain the power load analysis results of the target power flow data.

[0039] Based on the above, please refer to the following: Figure 3 The diagram illustrates a power load analysis system 300 based on big data, including a processor 310 and a memory 320 that communicate with each other. The processor 310 is used to read computer programs from the memory 320 and execute them to implement the method described above.

[0040] Based on the above, a computer-readable storage medium is also provided, on which a computer program stored implements the above method during runtime.

[0041] In summary, based on the above scheme, by obtaining the power detection information of the target power flow data and performing current load analysis on the power detection information, the current flow events expressed by the power detection information are determined. Based on these current flow events, it is possible to determine whether there is a current overload problem in the target power flow data. This process avoids potential current overload issues that could lead to equipment malfunctions due to the need for detailed analysis of specific events in the target power flow data to address current overload problems. Therefore, real-time analysis of current overload is necessary to ensure the normal operation of the equipment.

[0042] It should be understood that the systems and modules described above can be implemented in various ways. For example, in some embodiments, the systems and modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the methods and systems described above can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this application can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the aforementioned hardware circuits and software (e.g., firmware).

[0043] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.

[0044] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore remain within the spirit and scope of the exemplary embodiments of this application.

[0045] Furthermore, this application uses specific terms to describe embodiments of the application. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of the application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of the application can be appropriately combined.

[0046] Furthermore, those skilled in the art will understand that aspects of this application can be described and illustrated through several patentable types or situations, including any new and useful combination of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, aspects of this application can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, aspects of this application may manifest as a computer product located on one or more computer-readable media, the product including computer-readable program code.

[0047] Computer storage media may contain a propagated data signal containing computer program code, for example, on baseband or as part of a carrier wave. This propagated signal may take various forms, including electromagnetic, optical, and suitable combinations thereof. Computer storage media can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to enable communication, propagation, or transmission of a program for use. The program code located on the computer storage medium can be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0048] The computer program code required for the operation of each part of this application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby, and Groovy, or other programming languages. This program code can run entirely on the user's computer, or as a standalone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as Software as a Service (SaaS).

[0049] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this application are not intended to limit the order of the processes and methods of this application. Although the foregoing disclosure has discussed some currently considered useful embodiments of the invention through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the substance and scope of the embodiments of this application. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely through software solutions, such as installing the described system on existing servers or mobile devices.

[0050] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this disclosure method does not imply that the subject matter of the application requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of the single embodiments disclosed above.

[0051] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are open to adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters are taken into account a specified number of significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of application in some embodiments of this application are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0052] For each patent, patent application, patent application publication, and other material such as articles, books, specifications, publications, and documents referenced in this application, the entire contents of that patent are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this application, as well as documents that limit the broadest scope of the claims in this application (currently or subsequently appended to this application). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or terminology used in the supplementary materials of this application and the content of this application, the descriptions, definitions, and / or terminology used in this application shall prevail.

[0053] Finally, it should be understood that the embodiments described in this application are merely illustrative of the principles of the embodiments of this application. Other modifications may also fall within the scope of this application. Therefore, alternative configurations of the embodiments of this application are considered as examples and not limitations, and are regarded as consistent with the teachings of this application. Accordingly, the embodiments of this application are not limited to the embodiments explicitly described and illustrated in this application.

[0054] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A power load analysis method based on big data, characterized in that, The method includes at least: Obtain target power flow data, wherein the target power flow data is an event waiting to be read for power consumption; Obtain power detection information of the target power flow data, wherein the power detection information is generated by the detection platform when performing current detection on the target power flow data; A current load analysis is performed on the power detection information to determine the current load analysis result. The current load analysis result is used to represent the correlation between the power detection information and the pending power reading result. The pending power reading result is used to represent the power load relationship existing in the target power flow data. Based on the current load analysis results, the power load analysis results of the target power flow data are obtained; The step of obtaining the power load analysis results of the target power flow data by combining the current load analysis results includes: In response to the current load analysis results indicating that there is a correlation between the pending power reading results and the default correlation condition, the target power flow data is described saliencyfully. The target power flow data is loaded into the optimization matrix and optimized in conjunction with the saliency description to obtain the power load analysis results of the target power flow data; The step of performing current load analysis on the power detection information and determining the current load analysis results includes: Input the power detection information into the current load analysis thread; The current load analysis thread performs current load analysis on the power detection information and outputs the current load analysis results. The current load analysis thread is a current analysis thread pre-configured through a historical database. The process of performing current load analysis on the power detection information based on the current load analysis thread and outputting the current load analysis results includes: Based on the current load analysis thread, the power detection information is convolutionally processed and information optimization is performed to determine the power detection information description. The power detection information is input into the trigger thread, and the correlation between the pending power reading result and the power detection information is output and determined as the current load analysis result.

2. The method according to claim 1, characterized in that, The method further includes: Obtain example power detection information, which includes events described by objects in the power flow data architecture for detecting power flow data; The example power detection information is updated to determine the example data; Configure the current load analysis thread based on the example data until the analysis results of the current load analysis thread meet the specified analysis instructions.

3. The method according to claim 2, characterized in that, The step of updating the example power detection information and determining the example data includes: The example power detection information is filtered based on a specified filtering method to determine the filtered example data. Based on a specified optimization method, the example power detection information is optimized to determine the optimized example data; The example data is obtained by combining the filtered example data and the optimized example data.

4. The method according to claim 3, characterized in that, The specified filtering method includes one or more of the following methods: Select example power detection information where the number of first default nodes is less than a specified number or a specified confidence level; Select the first node type and the second node type to update the example power detection information; Select example power detection information where the number of similar events is greater than a specified number; Select example power detection information where the number of second default nodes exceeds the number of constraints or the constraint confidence level; Select the same example of power detection information from each first node in the event; Select examples of battery detection information where the content of a specific event cannot be read.

5. The method according to claim 3, characterized in that, The specified optimization method includes one or more of the following methods: The example power detection information is debugged based on the specified debugging method; After decompressing the example battery detection information belonging to the first topic into the second topic, it is then recompressed into the first topic. The text in the example power detection information is corrected based on the specified correction method.

6. The method according to claim 1, characterized in that, The method further includes: in response to the current load analysis result indicating that the correlation of the pending power reading results is lower than the default correlation, acquiring the target power flow data as an event that satisfies the analysis indication.

7. A power load analysis system based on big data, characterized in that, The method includes a processor and a memory that communicate with each other, the processor being configured to read a computer program from the memory and execute it to implement the method of any one of claims 1-6.

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